Patient-Specific Tumor Models for Rapid Personalized Drug Screening
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Solution Overview
Problem
Current cancer treatment methods are inefficient and costly due to the lack of personalized approaches, with less than 1% of oncology drugs progressing to clinical trials, and existing preclinical models like organoids and PDXs take too long to develop, limiting precision medicine.
Innovation Solution
Development of patient-specific clinical trials using computational models and rapid drug screening on patient-derived tumor models, such as organoids and PDXs, to identify effective treatments based on individual patient mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient-derived preclinical cancer models (organoids, PDXs) are used for precision medicine, then treatment personalization is improved, but development time increases to months
Solution Approach 1:
The patent applies preliminary action by pre-characterizing patient-derived models with comprehensive genomic sequencing and establishing baseline drug sensitivity profiles before clinical treatment decisions are needed. This advance preparation allows rapid matching of patient mutations to targeted therapies without waiting for model development during urgent treatment scenarios.
Solution Approach 2:
The patent uses copying by creating simplified computational models and databases that replicate the complex biological behavior of patient-derived organoids and PDXs. These digital twins enable rapid in silico drug screening that mimics in vivo results, providing treatment predictions in days rather than months while maintaining personalization accuracy.
2Reliability
If comprehensive drug screening is performed on patient-specific models, then treatment efficacy is improved, but time and resource consumption increases
Solution Approach 1:
The patent applies segmentation by dividing the drug screening process into hierarchical stages: first filtering compounds based on patient-specific genomic mutations to identify relevant pathways, then screening only drugs targeting those pathways, and finally validating top candidates in patient-derived models. This segmented approach maintains comprehensive evaluation while reducing overall screening time and resource requirements.
Solution Approach 2:
The patent changes parameters by dynamically adjusting screening criteria based on patient-specific molecular characteristics. The system modifies drug concentration ranges, exposure times, and combination protocols according to the patient's mutation profile and tumor biology, enabling efficient identification of effective doses without exhaustive testing of all possible conditions.
3Ease of operation
If standard of care treatment combinations are used for metastatic colorectal cancer, then treatment simplicity is maintained, but response rate decreases to less than 50%
Solution Approach 1:
The patent applies local quality by maintaining simple standard of care treatments for patients with common mutations while providing personalized targeted therapies for patients with specific actionable alterations. This localized customization approach preserves treatment simplicity for the majority of patients while improving response rates for those who can benefit from precision medicine based on their individual molecular profiles.
Data Source
AI summary
Methods of treatment and patient specific clinical trials are disclosed. According to an aspect, a method includes generating a patient specific tumor model. The method also includes testing one or more drugs on the patient specific tumor model. Further, the method includes treating a patient based on the results of the patient specific tumor model tests.